Several organizations unwittingly share production data for testing purposes in non-production environment. This increases the risk of data breach. Masking sensitive data is a heuristic approach that organizations adopt in order to comply with data privacy mandates. Current data masking tools have been used by organizations to share data in non-production environment in order to maintain high data utility and non-disclosure of customer sensitive data. These tools offer a variety of masking techniques that are capable of masking sensitive data to meet several data privacy expectations. However, these tools mask data by using a static predefined lookup for replacing the original data. Using such techniques (or tools), it is easy to reproduce (or decipher) the original data and hence it is less secure leading to compromise in utility and privacy. Further, existing solutions require data to be specified within a specific range.
Additionally, existing solutions work on physical look ups of original and masked data mapping, which requires a computing system to consume more disk space, and more time in terms of creating, updating and processing of the data in a presentable and secured format. It is therefore a challenge in maintaining consistency of data sharing at an enterprise level without disrupting the format of the data.